K
K.I. Chang
Researcher at University of Notre Dame
Publications - 19
Citations - 4463
K.I. Chang is an academic researcher from University of Notre Dame. The author has contributed to research in topics: Facial recognition system & Three-dimensional face recognition. The author has an hindex of 14, co-authored 19 publications receiving 4301 citations. Previous affiliations of K.I. Chang include Philips & University of South Florida.
Papers
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Journal ArticleDOI
A survey of approaches and challenges in 3D and multi-modal 3D+2D face recognition
TL;DR: This survey focuses on recognition performed by matching models of the three-dimensional shape of the face, either alone or in combination with matching corresponding two-dimensional intensity images.
Book ChapterDOI
Current Status of the Digital Database for Screening Mammography
Michael D. Heath,Kevin W. Bowyer,Daniel B. Kopans,W. Philip Kegelmeyer,Richard H. Moore,K.I. Chang,S. Munishkumaran +6 more
TL;DR: The Digital Database for Screening Mammography is a resource for use by researchers investigating mammogram image analysis, focused on the context of image analysis to aid in screening for breast cancer.
Journal ArticleDOI
Comparison and combination of ear and face images in appearance-based biometrics
TL;DR: It is found that recognition performance is not significantly different between the face and the ear, for example, 70.5 percent versus 71.6 percent in one experiment and multimodal recognition using both the ear and face results in statistically significant improvement over either individual biometric.
Journal ArticleDOI
An evaluation of multimodal 2D+3D face biometrics
TL;DR: The largest experimental study to date in multimodal 2D+3D face recognition, involving 198 persons in the gallery and either 198 or 670 time-lapse probe images, reaches major conclusions.
Journal ArticleDOI
Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
TL;DR: This is the first approach to use multiple overlapping regions around the nose to handle the problem of expression variation and shows substantial improvement over matching the shape of a single larger frontal face region.